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The course covers the basics of parallel programming and distributed processing of computationally intensive tasks and procedures. The aim of the course is to analyze the process communication and time complexity of process subtasks with respect to design the procedures with optimized parallel performance. Implementation of the studied methods is predominantly in the JAVA programming language.
Last update: Cejnar Pavel (30.07.2013)
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Students will be able to:
Last update: Cejnar Pavel (30.07.2013)
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To complete the course, student must successfully pass an exam test covering the student's practical skills and pass the oral exam covering the theoretical knowledge. Last update: Cejnar Pavel (26.04.2018)
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R: Tvrdík, P.: Parallel Algorithms and Computing. ČVUT Praha, 2010. ISBN 978-80-01-04333-2. A: Lea, D.: Concurrent Programming in Java: Design Principles and Pattern (2nd Edition). Prentice Hall, 1999. ISBN 978-0-201-31009-2. A: Eckel, B.: Thinking in Java. Prentice Hall, 2006. ISBN 978-0131872486. A: Patterson, A. D., Henessy, L. J.: Computer Organization and Design, Fourth Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design).Morgan Kaufmann, 2011. ISBN 978-0123747501. A: Wilkinson, B., Allen, M.: Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers. Prentice Hall, 1999.ISBN 0-13-671710-1. Last update: Cejnar Pavel (25.04.2018)
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Lectures and courses. Last update: Cejnar Pavel (30.07.2013)
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1. Software optimization of computer procedures. Distributed and parallel data processing. Parallelization on multicore computer processors. 2. Simple synchronization operations and synchronization blocking primitives. 3. Synchronization model problems. Synchronization support in programming language. 4. Algorithms asymptotic sequential and parallel time and space complexity and their application. 5. PRAM parallel computation model and basic terms of parallel computation theory. Recursion procedures and their parallelization. Parallel matrix multiplication. 6. Parallel sort algorithms. 7. Parallel prefix sum algorithm and its applications (RadixSort, parallel addition, SPPS). 8. Parallelization of graph algorithms and linear algebra algorithms. Available parallel implementations of mathematical libraries. 9. Distributed data processing and communications. Remote procedures call. 10. Current hardware architectures for parallel data computation. 11. CASE STUDY: distributed and parallel data processing in MATLAB. 12. CASE STUDY: distributed and parallel data processing in C++ in Unix environment. 13. CASE STUDY: distributed and parallel data processing using GPU. 14. Advanced parallelization techniques. Distributed and parallel processing optimization. Last update: Cejnar Pavel (30.07.2013)
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Last update: Cejnar Pavel (06.02.2018)
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Last update: Cejnar Pavel (30.07.2013)
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Teaching methods | ||||
Activity | Credits | Hours | ||
Konzultace s vyučujícími | 0.1 | 2 | ||
Účast na přednáškách | 1 | 28 | ||
Práce na individuálním projektu | 0.5 | 14 | ||
Příprava na zkoušku a její absolvování | 1.4 | 40 | ||
Účast na seminářích | 1 | 28 | ||
4 / 4 | 112 / 112 |
Coursework assessment | |
Form | Significance |
Regular attendance | 40 |
Examination test | 30 |
Oral examination | 30 |